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Research on 3D point cloud alignment algorithm based on SHOT features.

Zheng Fu1, Enzhong Zhang1, Ruiyang Sun1

  • 1School of Mechatronical Engineering, Changchun University of Technology, Changchun, China.

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|March 27, 2024
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Summary
This summary is machine-generated.

This study introduces an improved point cloud registration method using normal vector and directional histogram features (SHOT). The novel approach significantly reduces iterations and enhances feature point extraction for better 3D data alignment.

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Area of Science:

  • Computer Vision
  • 3D Data Processing
  • Geometric Algorithms

Background:

  • Traditional Iterative Closest Point (ICP) algorithms require precise initial positioning.
  • Challenges exist in handling noisy and incomplete point cloud data.
  • Feature extraction and alignment are critical steps in point cloud registration.

Purpose of the Study:

  • To develop a robust point cloud registration method that overcomes the limitations of traditional ICP.
  • To improve the accuracy and efficiency of point cloud alignment, especially with noisy or incomplete data.
  • To reduce the computational cost and number of iterations required for registration.

Main Methods:

  • A hybrid filtering method based on voxelization for noise reduction.
  • Optimization of feature point extraction to handle missing point cloud sections.
  • A fine alignment strategy utilizing Scalar Histogram Of Oriented Gradients (SHOT) features.

Main Results:

  • Achieved high noise removal rates (97.5%, 97.8%, 93.8%) using the hybrid filtering method.
  • The optimized feature extraction effectively processed incomplete point cloud data.
  • Reduced iteration count by 40.23% and 37.62% on benchmark and self-measured datasets, respectively.

Conclusions:

  • The proposed SHOT-based point cloud registration method offers improved efficiency and robustness.
  • The hybrid filtering and optimized feature extraction contribute to superior performance.
  • This method provides a significant advancement over traditional point cloud registration techniques.